Our primary goal is to build multiple local models that represent small and coherent sub-communities. The existing studies [1]}, [2]}, [3]}, [4]}, [5]} developed local models for this purpose; however, they have limited potential. LLORMA and its variants [1]}, [2]}, [3]} showed superior performance over a global model, but their local models covered a broad range of an entire matrix, where the local models with the wide coverage were comparable to the global model. As discussed in [9]}, the improvement of LLORMA is statistically significant over an ensemble method; however, it is also true that the improvement mostly comes from the ensemble effect, not just from the characteristics of local models. Moreover, training local models with small sub-matrices in [4]}, [5]} suffers from the lack of training data, resulting in low accuracy; The global model largely compensates for the lack of training data issues in local models, which does not adhere to the effect of local models.
<FIGURE>